The present invention relates generally to the field of health-related data collection devices, and more particularly to the field of devices for collection of data related to hand movement and/or musculature. Touch screens are used more and more often by all different age of groups of people. People use the touch screen devices for various purposes, such as reading, emailing, chatting and making phone calls.
As stated at the Abstract of the article “Detection of Motor Impairment in Parkinson's Disease via Mobile Touchscreen Typing” by Teresa Arroyo-Gallego et al. (published by IEEE, 2016) “Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and progression of the disease. Mobile technology can help clinical decision making by completing the information of motor status between hospital visits. This paper presents an algorithm to detect PD by analyzing the typing activity on smartphones independently of the content of the typed text. We propose a set of touchscreen typing features based on a covariance, skewness and kurtosis analysis of the timing information of the data to capture PD motor signs . . . . This work contributes to the development of a home-based, high-compliance and high-frequency PD motor test by analysis of routine typing on touchscreens.”